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Virtual presentation / poster accept

Deep Ensembles for Graphs with Higher-order Dependencies

Steven Krieg · William Burgis · Patrick Soga · Nitesh Chawla

Keywords: [ Higher Order Networks ] [ Deep Ensembles ] [ graph neural networks ] [ representation learning ] [ semisupervised learning ] [ Deep Learning and representational learning ]


Abstract:

Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure. In the presence of higher-order sequential dependencies, we show that the tendency of traditional graph representations to underfit each node's neighborhood causes existing GNNs to generalize poorly. To address this, we propose a novel Deep Graph Ensemble (DGE), which captures neighborhood variance by training an ensemble of GNNs on different neighborhood subspaces of the same node within a higher-order network structure. We show that DGE consistently outperforms existing GNNs on semisupervised and supervised tasks on six real-world data sets with known higher-order dependencies, even under a similar parameter budget. We demonstrate that learning diverse and accurate base classifiers is central to DGE's success, and discuss the implications of these findings for future work on GNNs.

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